'''simple docstring'''





import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:	Dict    =logging.get_logger(__name__)





def       lowerCAmelCase_							(   snake_case_			:					str     )       -> int:


     '''simple docstring'''




     UpperCAmelCase_			       =  SwinConfig.from_pretrained(
         "microsoft/swin-tiny-patch4-window7-224"   ,       out_features=["stage1", "stage2", "stage3", "stage4"]     )
     UpperCAmelCase_			       =  MaskFormerConfig(backbone_config=snake_case_     )

     UpperCAmelCase_			       =  "huggingface/label-files"
     if "ade20k-full" in model_name:
          # this should be ok
          UpperCAmelCase_			       =  8_47
          UpperCAmelCase_			       =  "maskformer-ade20k-full-id2label.json"
     elif "ade" in model_name:
          # this should be ok
          UpperCAmelCase_			       =  1_50
          UpperCAmelCase_			       =  "ade20k-id2label.json"
     elif "coco-stuff" in model_name:
          # this should be ok
          UpperCAmelCase_			       =  1_71
          UpperCAmelCase_			       =  "maskformer-coco-stuff-id2label.json"
     elif "coco" in model_name:
          # TODO
          UpperCAmelCase_			       =  1_33
          UpperCAmelCase_			       =  "coco-panoptic-id2label.json"
     elif "cityscapes" in model_name:
          # this should be ok
          UpperCAmelCase_			       =  19
          UpperCAmelCase_			       =  "cityscapes-id2label.json"
     elif "vistas" in model_name:
          # this should be ok
          UpperCAmelCase_			       =  65
          UpperCAmelCase_			       =  "mapillary-vistas-id2label.json"

     UpperCAmelCase_			       =  json.load(open(hf_hub_download(snake_case_   ,       snake_case_   ,       repo_type="dataset"     )   ,       "r"     )     )
     UpperCAmelCase_			       =  {int(snake_case_     ): v for k, v in idalabel.items()}

     return config





def       lowerCAmelCase_							(   snake_case_			:					List[str]     )       -> List[Any]:


     '''simple docstring'''




     UpperCAmelCase_			       =  []
     # stem
     # fmt: off
     rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight")     )
     rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias")     )
     rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight")     )
     rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.bias")     )
     # stages
     for i in range(len(config.backbone_config.depths     )     ):
          for j in range(config.backbone_config.depths[i]     ):
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")     )
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")     )
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")     )
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")     )
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")     )
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")     )
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")     )
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")     )
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")     )
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")     )
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")     )
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")     )

          if i < 3:
               rename_keys.append((f"""backbone.layers.{i}.downsample.reduction.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")     )
               rename_keys.append((f"""backbone.layers.{i}.downsample.norm.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")     )
               rename_keys.append((f"""backbone.layers.{i}.downsample.norm.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")     )
          rename_keys.append((f"""backbone.norm{i}.weight""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")     )
          rename_keys.append((f"""backbone.norm{i}.bias""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")     )

     # FPN
     rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight")     )
     rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight")     )
     rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias")     )
     for source_index, target_index in zip(range(3   ,       0   ,       -1     )   ,       range(0   ,       3     )     ):
          rename_keys.append((f"""sem_seg_head.adapter_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")     )
          rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")     )
          rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")     )
          rename_keys.append((f"""sem_seg_head.layer_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")     )
          rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")     )
          rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")     )
     rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight")     )
     rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias")     )

     # Transformer decoder
     for idx in range(config.decoder_config.decoder_layers     ):
          # self-attention out projection
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")     )
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")     )
          # cross-attention out projection
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")     )
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")     )
          # MLP 1
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")     )
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")     )
          # MLP 2
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")     )
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")     )
          # layernorm 1 (self-attention layernorm)
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")     )
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")     )
          # layernorm 2 (cross-attention layernorm)
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")     )
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")     )
          # layernorm 3 (final layernorm)
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")     )
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")     )

     rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight")     )
     rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias")     )

     # heads on top
     rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight")     )

     rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight")     )
     rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias")     )

     rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight")     )
     rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias")     )

     for i in range(3     ):
          rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", f"""mask_embedder.{i}.0.weight""")     )
          rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", f"""mask_embedder.{i}.0.bias""")     )
     # fmt: on

     return rename_keys





def       lowerCAmelCase_							(   snake_case_			:					Dict   ,       snake_case_			:					Dict   ,       snake_case_			:					Any     )       -> Any:


     '''simple docstring'''




     UpperCAmelCase_			       =  dct.pop(snake_case_     )
     UpperCAmelCase_			       =  val





def       lowerCAmelCase_							(   snake_case_			:					List[str]   ,       snake_case_			:					List[str]     )       -> List[str]:


     '''simple docstring'''




     UpperCAmelCase_			       =  [int(backbone_config.embed_dim * 2**i     ) for i in range(len(backbone_config.depths     )     )]
     for i in range(len(backbone_config.depths     )     ):
          UpperCAmelCase_			       =  num_features[i]
          for j in range(backbone_config.depths[i]     ):
               # fmt: off
               # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
               UpperCAmelCase_			       =  state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""     )
               UpperCAmelCase_			       =  state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""     )
               # next, add query, keys and values (in that order) to the state dict
               UpperCAmelCase_			       =  in_proj_weight[:dim, :]
               UpperCAmelCase_			       =  in_proj_bias[: dim]
               UpperCAmelCase_			       =  in_proj_weight[
                   dim : dim * 2, :
               ]
               UpperCAmelCase_			       =  in_proj_bias[
                   dim : dim * 2
               ]
               UpperCAmelCase_			       =  in_proj_weight[
                   -dim :, :
               ]
               UpperCAmelCase_			       =  in_proj_bias[-dim :]
               # fmt: on





def       lowerCAmelCase_							(   snake_case_			:					Dict   ,       snake_case_			:					str     )       -> str:


     '''simple docstring'''




     UpperCAmelCase_			       =  config.decoder_config.hidden_size
     for idx in range(config.decoder_config.decoder_layers     ):
          # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
          UpperCAmelCase_			       =  state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""     )
          UpperCAmelCase_			       =  state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""     )
          # next, add query, keys and values (in that order) to the state dict
          UpperCAmelCase_			       =  in_proj_weight[: hidden_size, :]
          UpperCAmelCase_			       =  in_proj_bias[:config.hidden_size]
          UpperCAmelCase_			       =  in_proj_weight[hidden_size : hidden_size * 2, :]
          UpperCAmelCase_			       =  in_proj_bias[hidden_size : hidden_size * 2]
          UpperCAmelCase_			       =  in_proj_weight[-hidden_size :, :]
          UpperCAmelCase_			       =  in_proj_bias[-hidden_size :]
          # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
          UpperCAmelCase_			       =  state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""     )
          UpperCAmelCase_			       =  state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""     )
          # next, add query, keys and values (in that order) to the state dict
          UpperCAmelCase_			       =  in_proj_weight[: hidden_size, :]
          UpperCAmelCase_			       =  in_proj_bias[:config.hidden_size]
          UpperCAmelCase_			       =  in_proj_weight[hidden_size : hidden_size * 2, :]
          UpperCAmelCase_			       =  in_proj_bias[hidden_size : hidden_size * 2]
          UpperCAmelCase_			       =  in_proj_weight[-hidden_size :, :]
          UpperCAmelCase_			       =  in_proj_bias[-hidden_size :]
     # fmt: on





def       lowerCAmelCase_							(   )       -> torch.Tensor:


     '''simple docstring'''




     UpperCAmelCase_			       =  "http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCAmelCase_			       =  Image.open(requests.get(snake_case_   ,       stream=snake_case_     ).raw     )
     return im





@torch.no_grad()
def       lowerCAmelCase_							(   snake_case_			:					str   ,       snake_case_			:					str   ,       snake_case_			:					str   ,       snake_case_			:					bool = False     )       -> List[Any]:


     '''simple docstring'''




     UpperCAmelCase_			       =  get_maskformer_config(snake_case_     )

     # load original state_dict
     with open(snake_case_   ,       "rb"     ) as f:
          UpperCAmelCase_			       =  pickle.load(snake_case_     )
     UpperCAmelCase_			       =  data["model"]

     # for name, param in state_dict.items():
     #     print(name, param.shape)

     # rename keys
     UpperCAmelCase_			       =  create_rename_keys(snake_case_     )
     for src, dest in rename_keys:
          rename_key(snake_case_   ,       snake_case_   ,       snake_case_     )
     read_in_swin_q_k_v(snake_case_   ,       config.backbone_config     )
     read_in_decoder_q_k_v(snake_case_   ,       snake_case_     )

     # update to torch tensors
     for key, value in state_dict.items():
          UpperCAmelCase_			       =  torch.from_numpy(snake_case_     )

     # load 🤗 model
     UpperCAmelCase_			       =  MaskFormerForInstanceSegmentation(snake_case_     )
     model.eval()

     for name, param in model.named_parameters():
          print(snake_case_   ,       param.shape     )

     UpperCAmelCase_       ,					UpperCAmelCase_			       =  model.load_state_dict(snake_case_   ,       strict=snake_case_     )
     assert missing_keys == [
         "model.pixel_level_module.encoder.model.layernorm.weight",
         "model.pixel_level_module.encoder.model.layernorm.bias",
     ]
     assert len(snake_case_     ) == 0, f"""Unexpected keys: {unexpected_keys}"""

     # verify results
     UpperCAmelCase_			       =  prepare_img()
     if "vistas" in model_name:
          UpperCAmelCase_			       =  65
     elif "cityscapes" in model_name:
          UpperCAmelCase_			       =  6_55_35
     else:
          UpperCAmelCase_			       =  2_55
     UpperCAmelCase_			       =  True if "ade" in model_name else False
     UpperCAmelCase_			       =  MaskFormerImageProcessor(ignore_index=snake_case_   ,       reduce_labels=snake_case_     )

     UpperCAmelCase_			       =  image_processor(snake_case_   ,       return_tensors="pt"     )

     UpperCAmelCase_			       =  model(**snake_case_     )

     print("Logits:"   ,       outputs.class_queries_logits[0, :3, :3]     )

     if model_name == "maskformer-swin-tiny-ade":
          UpperCAmelCase_			       =  torch.tensor(
              [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]     )
     assert torch.allclose(outputs.class_queries_logits[0, :3, :3]   ,       snake_case_   ,       atol=1E-4     )
     print("Looks ok!"     )

     if pytorch_dump_folder_path is not None:
          print(f"""Saving model and image processor to {pytorch_dump_folder_path}"""     )
          Path(snake_case_     ).mkdir(exist_ok=snake_case_     )
          model.save_pretrained(snake_case_     )
          image_processor.save_pretrained(snake_case_     )

     if push_to_hub:
          print("Pushing model and image processor to the hub..."     )
          model.push_to_hub(f"""nielsr/{model_name}"""     )
          image_processor.push_to_hub(f"""nielsr/{model_name}"""     )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:	List[Any]    =argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--model_name',
        default='maskformer-swin-tiny-ade',
        type=str,
        help=('Name of the MaskFormer model you\'d like to convert',),
    )
    parser.add_argument(
        '--checkpoint_path',
        default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
        type=str,
        help='Path to the original state dict (.pth file).',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
    )
    parser.add_argument(
        '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
    )

    SCREAMING_SNAKE_CASE_:	Tuple    =parser.parse_args()
    convert_maskformer_checkpoint(
        args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )












"""simple docstring"""
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


snake_case : Any     		=		logging.getLogger(__name__)
snake_case : Optional[Any]     		=		list(MODEL_FOR_MASKED_LM_MAPPING.keys())
snake_case : Optional[Any]     		=		tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class        UpperCamelCase__ :







    """simple docstring"""
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
            )
        }							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(a_)}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """Override some existing default config settings when a model is trained from scratch. Example: """
                """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
            )
        }							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}							,	)
    __UpperCAmelCase           = field(
        default="""main"""							,	metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                """with private models)."""
            )
        }							,	)







    def 				a__			(		self :	Optional[int] ):





           '''simple docstring'''

           if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
                  raise ValueError(
                      '--config_overrides can\'t be used in combination with --config_name or --model_name_or_path' )



@dataclass
class        UpperCamelCase__ :







    """simple docstring"""
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """The name of the dataset to use (via the datasets library)."""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
    __UpperCAmelCase           = field(default=a_							,	metadata={"""help""": """The input training data file (a text file)."""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Overwrite the cached training and evaluation sets"""})
    __UpperCAmelCase           = field(
        default=5							,	metadata={
            """help""": """The percentage of the train set used as validation set in case there's no validation split"""
        }							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """The maximum total input sequence length after tokenization. Sequences longer """
                """than this will be truncated. Default to the max input length of the model."""
            )
        }							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """The number of processes to use for the preprocessing."""}							,	)
    __UpperCAmelCase           = field(
        default=0.15							,	metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """Whether to pad all samples to `max_seq_length`. """
                """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
            )
        }							,	)







    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           if self.train_file is not None:
                  __magic_name__      				=    self.train_file.split('.' )[-1]
                  assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
           if self.validation_file is not None:
                  __magic_name__      				=    self.validation_file.split('.' )[-1]
                  assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."


def 							A						(				__snake_case: Tuple ,     __snake_case: str  )    ->		Tuple:

       """simple docstring"""





       with open(__snake_case ,     'r' ,     encoding='utf-8'  ) as f:
              __magic_name__      				=    [json.loads(__snake_case  ) for line in f.read().splitlines() if (len(__snake_case  ) > 0 and not line.isspace())]
       assert len(__snake_case  ) == len(__snake_case  )

       __magic_name__      				=    {c: dataset[c] for c in dataset.column_names}
       __magic_name__      				=    refs
       return Dataset.from_dict(__snake_case  )


def 							A						(				)    ->		Union[str, Any]:

       """simple docstring"""





       __magic_name__      				=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
       if len(sys.argv  ) == 2 and sys.argv[1].endswith('.json'  ):
              # If we pass only one argument to the script and it's the path to a json file,
              # let's parse it to get our arguments.
              __magic_name__				,  __magic_name__				,  __magic_name__      				=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
       else:
              __magic_name__				,  __magic_name__				,  __magic_name__      				=    parser.parse_args_into_dataclasses()

       # Detecting last checkpoint.
       __magic_name__      				=    None
       if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
              __magic_name__      				=    get_last_checkpoint(training_args.output_dir  )
              if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
                     raise ValueError(
                         F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
                         'Use --overwrite_output_dir to overcome.'  )
              elif last_checkpoint is not None:
                     logger.info(
                         F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
                         'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'  )

    # Setup logging
       logging.basicConfig(
           format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' ,     datefmt='%m/%d/%Y %H:%M:%S' ,     handlers=[logging.StreamHandler(sys.stdout  )] ,     )
       logger.setLevel(logging.INFO if is_main_process(training_args.local_rank  ) else logging.WARN  )

       # Log on each process the small summary:
       logger.warning(
           F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
           + F"""distributed training: {bool(training_args.local_rank != -1  )}, 16-bits training: {training_args.fpaa}"""  )
       # Set the verbosity to info of the Transformers logger (on main process only):
       if is_main_process(training_args.local_rank  ):
              transformers.utils.logging.set_verbosity_info()
              transformers.utils.logging.enable_default_handler()
              transformers.utils.logging.enable_explicit_format()
       logger.info('Training/evaluation parameters %s' ,     __snake_case  )

       # Set seed before initializing model.
       set_seed(training_args.seed  )

       # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
       # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
       # (the dataset will be downloaded automatically from the datasets Hub).
       #
       # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
       # 'text' is found. You can easily tweak this behavior (see below).
       #
       # In distributed training, the load_dataset function guarantee that only one local process can concurrently
       # download the dataset.
       if data_args.dataset_name is not None:
              # Downloading and loading a dataset from the hub.
              __magic_name__      				=    load_dataset(data_args.dataset_name ,     data_args.dataset_config_name  )
              if "validation" not in datasets.keys():
                     __magic_name__      				=    load_dataset(
                         data_args.dataset_name ,     data_args.dataset_config_name ,     split=F"""train[:{data_args.validation_split_percentage}%]""" ,     )
                     __magic_name__      				=    load_dataset(
                         data_args.dataset_name ,     data_args.dataset_config_name ,     split=F"""train[{data_args.validation_split_percentage}%:]""" ,     )
       else:
              __magic_name__      				=    {}
              if data_args.train_file is not None:
                     __magic_name__      				=    data_args.train_file
              if data_args.validation_file is not None:
                     __magic_name__      				=    data_args.validation_file
              __magic_name__      				=    data_args.train_file.split('.'  )[-1]
              if extension == "txt":
                     __magic_name__      				=    'text'
              __magic_name__      				=    load_dataset(__snake_case ,     data_files=__snake_case  )
       # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
       # https://huggingface.co/docs/datasets/loading_datasets.html.

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.
       __magic_name__      				=    {
           'cache_dir': model_args.cache_dir,
           'revision': model_args.model_revision,
           'use_auth_token': True if model_args.use_auth_token else None,
       }
       if model_args.config_name:
              __magic_name__      				=    AutoConfig.from_pretrained(model_args.config_name ,     **__snake_case  )
       elif model_args.model_name_or_path:
              __magic_name__      				=    AutoConfig.from_pretrained(model_args.model_name_or_path ,     **__snake_case  )
       else:
              __magic_name__      				=    CONFIG_MAPPING[model_args.model_type]()
              logger.warning('You are instantiating a new config instance from scratch.'  )
              if model_args.config_overrides is not None:
                     logger.info(F"""Overriding config: {model_args.config_overrides}"""  )
                     config.update_from_string(model_args.config_overrides  )
                     logger.info(F"""New config: {config}"""  )

       __magic_name__      				=    {
           'cache_dir': model_args.cache_dir,
           'use_fast': model_args.use_fast_tokenizer,
           'revision': model_args.model_revision,
           'use_auth_token': True if model_args.use_auth_token else None,
       }
       if model_args.tokenizer_name:
              __magic_name__      				=    AutoTokenizer.from_pretrained(model_args.tokenizer_name ,     **__snake_case  )
       elif model_args.model_name_or_path:
              __magic_name__      				=    AutoTokenizer.from_pretrained(model_args.model_name_or_path ,     **__snake_case  )
       else:
              raise ValueError(
                  'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
                  'You can do it from another script, save it, and load it from here, using --tokenizer_name.'  )

       if model_args.model_name_or_path:
              __magic_name__      				=    AutoModelForMaskedLM.from_pretrained(
                  model_args.model_name_or_path ,     from_tf=bool('.ckpt' in model_args.model_name_or_path  ) ,     config=__snake_case ,     cache_dir=model_args.cache_dir ,     revision=model_args.model_revision ,     use_auth_token=True if model_args.use_auth_token else None ,     )
       else:
              logger.info('Training new model from scratch'  )
              __magic_name__      				=    AutoModelForMaskedLM.from_config(__snake_case  )

       model.resize_token_embeddings(len(__snake_case  )  )

       # Preprocessing the datasets.
       # First we tokenize all the texts.
       if training_args.do_train:
              __magic_name__      				=    datasets['train'].column_names
       else:
              __magic_name__      				=    datasets['validation'].column_names
       __magic_name__      				=    'text' if 'text' in column_names else column_names[0]

       __magic_name__      				=    'max_length' if data_args.pad_to_max_length else False

       def tokenize_function(__snake_case: str  ):
              # Remove empty lines
              __magic_name__      				=    [line for line in examples['text'] if len(__snake_case  ) > 0 and not line.isspace()]
              return tokenizer(examples['text'] ,     padding=__snake_case ,     truncation=__snake_case ,     max_length=data_args.max_seq_length  )

       __magic_name__      				=    datasets.map(
           __snake_case ,     batched=__snake_case ,     num_proc=data_args.preprocessing_num_workers ,     remove_columns=[text_column_name] ,     load_from_cache_file=not data_args.overwrite_cache ,     )

       # Add the chinese references if provided
       if data_args.train_ref_file is not None:
              __magic_name__      				=    add_chinese_references(tokenized_datasets['train'] ,     data_args.train_ref_file  )
       if data_args.validation_ref_file is not None:
              __magic_name__      				=    add_chinese_references(
                  tokenized_datasets['validation'] ,     data_args.validation_ref_file  )
       # If we have ref files, need to avoid it removed by trainer
       __magic_name__      				=    data_args.train_ref_file or data_args.validation_ref_file
       if has_ref:
              __magic_name__      				=    False

       # Data collator
       # This one will take care of randomly masking the tokens.
       __magic_name__      				=    DataCollatorForWholeWordMask(tokenizer=__snake_case ,     mlm_probability=data_args.mlm_probability  )

       # Initialize our Trainer
       __magic_name__      				=    Trainer(
           model=__snake_case ,     args=__snake_case ,     train_dataset=tokenized_datasets['train'] if training_args.do_train else None ,     eval_dataset=tokenized_datasets['validation'] if training_args.do_eval else None ,     tokenizer=__snake_case ,     data_collator=__snake_case ,     )

       # Training
       if training_args.do_train:
              if last_checkpoint is not None:
                     __magic_name__      				=    last_checkpoint
              elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path  ):
                     __magic_name__      				=    model_args.model_name_or_path
              else:
                     __magic_name__      				=    None
              __magic_name__      				=    trainer.train(resume_from_checkpoint=__snake_case  )
              trainer.save_model()  # Saves the tokenizer too for easy upload

              __magic_name__      				=    os.path.join(training_args.output_dir ,     'train_results.txt'  )
              if trainer.is_world_process_zero():
                     with open(__snake_case ,     'w'  ) as writer:
                            logger.info('***** Train results *****'  )
                            for key, value in sorted(train_result.metrics.items()  ):
                                   logger.info(F"""  {key} = {value}"""  )
                                   writer.write(F"""{key} = {value}\n"""  )

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
                     trainer.state.save_to_json(os.path.join(training_args.output_dir ,     'trainer_state.json'  )  )

    # Evaluation
       __magic_name__      				=    {}
       if training_args.do_eval:
              logger.info('*** Evaluate ***'  )

              __magic_name__      				=    trainer.evaluate()

              __magic_name__      				=    math.exp(eval_output['eval_loss']  )
              __magic_name__      				=    perplexity

              __magic_name__      				=    os.path.join(training_args.output_dir ,     'eval_results_mlm_wwm.txt'  )
              if trainer.is_world_process_zero():
                     with open(__snake_case ,     'w'  ) as writer:
                            logger.info('***** Eval results *****'  )
                            for key, value in sorted(results.items()  ):
                                   logger.info(F"""  {key} = {value}"""  )
                                   writer.write(F"""{key} = {value}\n"""  )

       return results


def 							A						(				__snake_case: List[str]  )    ->		Union[str, Any]:

       """simple docstring"""





       main()


if __name__ == "__main__":
 main()